Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A level set approach for computing solutions to incompressible two-phase flow
Journal of Computational Physics
International Journal of Computer Vision
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Level Set Based Segmentation with Intensity and Curvature Priors
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Approximations of Shape Metrics and Application to Shape Warping and Empirical Shape Statistics
Foundations of Computational Mathematics
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Prior Knowledge, Level Set Representations & Visual Grouping
International Journal of Computer Vision
IEEE Transactions on Image Processing
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Level set methods are effective for image segmentation problems. However, the methods suffer from limitations such as slow convergence and leaking problems. As such, over the past two decades, the original level set method has been evolved in many directions, including integration of prior shape models into the segmentation framework. In this paper, we introduce a new prior shape model for level set segmentation. With a shape model represented implicitly by a signed distance function, we incorporate a local shape parameter to the shape model. This parameter helps to regulate the model fitting process. Based on this local parameter of the shape model, we define a shape energy to drive the level set evolution for image segmentation. The shape energy is coupled with a Gaussian kernel, which acts as a weight distribution on the shape model. This Gaussian effect not only allows evolution of level set to deform around the shape model, but also provides a smoothing effect along the edges. Our approach presents a new dimension to extract local shape parameter directly from the shape model, which is different from previous work that focused on an indirect manner of feature extractions. Experimental results on synthetic, optical and MR images demonstrate the feasibility of this new shape model and shape energy.